code: def _evaluate_numeric(
      self, examples: list[NumericExample]
  ) -> dict[str, Sequence[float]]:
      prompts = [example.prompt for example in examples]
      tokenized_inputs = self.tokenizer(
          prompts,
          return_tensors="pt",
          truncation=True,
          return_token_type_ids=False,
      ).to(self.device)
      parser = BasicParser()
      # NOTE: this may need to change if we use batch size > 1 with padding
      outputs = self.model.generate(
          **tokenized_inputs,
          do_sample=True,
          num_return_sequences=10,
          max_new_tokens=7,
          temperature=0.5,
          pad_token_id=50526,
      )
      full_completions = self.tokenizer.batch_decode(
          outputs, skip_special_tokens=True
      )
      # strip out the prompt NOTE: again we're assuming the batch_size is 1
      untrimmed_completions = [
          fc[len(examples[0].prompt) :] for fc in full_completions
      ]
      # dropping anything after a new line
      completions = [
          comp.split("\n")[0] for comp in untrimmed_completions
      ]
      floats = parser(completions)
      # for now, we'll just take the mean of valid outputs as the estimate
      valid_floats = [f for f in floats if f is not None]
      if len(valid_floats) > 0:
          estimate = sum(valid_floats) / len(valid_floats)
      else:
          raise ValueError("No valid numbers returned")
      return {"estimate": [estimate]}
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from typing import Union, cast, Sequence
  from typing_extensions import Literal, get_args
  import numpy
  import torch
  import torch.nn.functional
  from transformers import AutoModelForCausalLM, AutoTokenizer
  from accelerate import Accelerator
  from .isp_dataset import ClassificationExample, Example, ExampleWithClasses, LogoddsExample, SequenceProbExample, NumericExample, TaskType
  from .isp_utils import BasicParser
  from .isp_utils import OpenAIModel, OpenaAIModelList
label_desc: Use image node_id 8 for calling the HFModel obj's underlying member method code with example usage: obj._evaluate_numeric(examples) and returns: dict